Cognitive and motor profiles as prodromal markers in predicting phenoconversion and phenotype in isolated REM sleep behavior disorder.
Bibliographic record
Abstract
OBJECTIVE: To determine the clinical markers based on cognitive and motor profiles in predicting phenoconverion and phenotype in isolated rapid eye movement sleep behavior disorder (iRBD). METHODS: 45 iRBD patients and 25 healthy controls were included in the follow-up study. All participates received comprehensive evaluations of cognitive, motor and autonomic function at baseline. Positive phenoconversion were identified according to standard diagnostic criteria during follow-up. RESULTS: 21 iRBD patients displayed phenoconversion in a mean follow-up of 2.9 ± 1.6 years, with 14 presenting motor phenotype and 7 cognitive phenotype. In iRBD, visuospatial, memory, attention-executive function, information processing speed, and motor function predicted phenoconversion, with the combination of Trail Making Test (TMT) and Alternate-tap Test (ATT) performing best (sensitivity = 95.0 %, specificity = 75.0 %); attention-executive function, information processing speed, and motor function predicted motor phenotype conversion, with the combination of TMT and ATT performing best (sensitivity = 100 %, specificity = 66.7 %); visuospatial, memory, and attention-executive function predicted cognitive phenotype conversion, with TMT performing best (sensitivity = 83.3 %, specificity = 91.7 %). Furthermore, individuals with lower z-scores of TMT, Symbol Digit Modalities Test, and ATT than the established cutoff values in iRBD exhibited a significantly higher risk for phenoconversion at follow-up (HR = 2.98, 9.53, 11.68; respectively). CONCLUSIONS: In iRBD, the attention-executive and motor function served as optimum combined markers in predicting phenoconversion and motor phenotype, whereas the attention-executive function performed best in predicting cognitive phenotype. Poor attention-executive function, information processing speed and motor function in iRBD independently increased the risk of phenoconversion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".